Platforms for the analysis of tissue-based quantitative protein biomarker assay studies and clinical diagnostics tend to have variable results due to operator interaction. In such systems, an operation may examine histology specimens, otherwise referred to herein as samples, using a microscope system configured to capture a magnified image of the specimens. Operator decision making and setup interactions, such as those related to image capture, may lead to quantitative error in later analysis. For certain immunohistochemistry (IHC) image analysis, the magnified image is captured with a digital camera for which an exposure time for image acquisition may be set manually.
Unfortunately, such a manual methodology introduces several significant limitations. For example, the operator may select what appears to be the highest expressing fields of view of a whole tissue section, or the highest expressing histospots of a TMA to determine exposure times that are then applied to all other images acquired during a particular test. Such an approach generally limits the overall dynamic range of an assay in that optimization of image acquisition for an expression level of a single sample may be to the detriment of the other samples. Additionally, different users may determine different initial exposure times for application across samples in a particular study or different fields of view or different histospots. In either instance, such operator-dependant variability introduces possible variations even when using the same system. Still further, there is a first operator interaction time related to examination of the sample and determination of a field of view (histospots) and a second operator interaction time related to exposure time selection and performance of the actual setup. In order to realize the greatest benefit of automatically produced consistent, quantitative data on an automated IHC analysis microscopy platform, any interaction of the user with the system, including a number of operator decisions, should be kept to a minimum.